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    Memristive Probabilistic Computing

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    Name:
    Alahmadi, Thesis_v4.pdf
    Size:
    2.515Mb
    Format:
    PDF
    Description:
    Final Thesis
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    Type
    Thesis
    Authors
    Alahmadi, Hamzah cc
    Advisors
    Salama, Khaled N. cc
    Committee members
    He, Jr-Hau cc
    Gao, Xin cc
    Program
    Electrical Engineering
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2017-10
    Permanent link to this record
    http://hdl.handle.net/10754/626192
    
    Metadata
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    Abstract
    In the era of Internet of Things and Big Data, unconventional techniques are rising to accommodate the large size of data and the resource constraints. New computing structures are advancing based on non-volatile memory technologies and different processing paradigms. Additionally, the intrinsic resiliency of current applications leads to the development of creative techniques in computations. In those applications, approximate computing provides a perfect fit to optimize the energy efficiency while compromising on the accuracy. In this work, we build probabilistic adders based on stochastic memristor. Probabilistic adders are analyzed with respect of the stochastic behavior of the underlying memristors. Multiple adder implementations are investigated and compared. The memristive probabilistic adder provides a different approach from the typical approximate CMOS adders. Furthermore, it allows for a high area saving and design exibility between the performance and power saving. To reach a similar performance level as approximate CMOS adders, the memristive adder achieves 60% of power saving. An image-compression application is investigated using the memristive probabilistic adders with the performance and the energy trade-off.
    DOI
    10.25781/KAUST-M506A
    ae974a485f413a2113503eed53cd6c53
    10.25781/KAUST-M506A
    Scopus Count
    Collections
    Theses; Theses; Electrical Engineering Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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